Training wideband acoustic models in the cepstral domain using mixed-bandwidth training data for speech recognition

• 1. 使用多级语料库数据增广进行驾驶舱语音识别声学模型训练的方法和系统 [P] . 中国专利： CN111833850A . 2020-10-27
• 2. 声学模型训练构造方法、及声学模型和语音识别系统 [P] . 中国专利： CN105609100B . 2019.08.20
• 3. TRAINING WIDEBAND ACOUSTIC MODELS IN THE CEPSTRAL DOMAIN USING MIXED-BANDWIDTH TRAINING DATA FOR SPEECH RECOGNITION [P] . 美国专利： US2010161332A1 . 2010-06-24

  机译： 混合带宽训练数据用于语音识别的倒谱域训练宽带声模型

• 4. Training wideband acoustic models in the cepstral domain using mixed-bandwidth training data and extended vectors for speech recognition [P] . 美国专利： US7454338B2 . 2008-11-18

  机译： 使用混合带宽训练数据和用于语音识别的扩展矢量在倒频谱域中训练宽带声学模型

• 5. Training wideband acoustic models in the cepstral domain using mixed-bandwidth training data and extended vectors for speech recognition [P] . 美国专利： US2006178875A1 . 2006-08-10

  机译： 使用混合带宽训练数据和用于语音识别的扩展矢量在倒频谱域中训练宽带声学模型

• 1. Study on Acoustic Modeling in a Mandarin Continuous Speech Recognition [J] . PENG Di, LIU Gang, GUO Jun 矿业科学技术（英文版） . 2007,第001期
• 2. Training Wideband Acoustic Models Using Mixed-Bandwidth Training Data for Speech Recognition [J] . Michael L. Seltzer, Alex Acero IEEE transactions on audio, speech and language processing . 2007,第1期
• 3. Distributed Training of Deep Neural Network Acoustic Models for Automatic Speech Recognition: A comparison of current training strategies [J] . Cui Xiaodong, Zhang Wei, Finkler Ulrich, IEEE Signal Processing Magazine . 2020,第3期
• 4. Training augmentation with TANDEM acoustic modelling in Punjabi adult speech recognition system [J] . Kadyan Virender, Bala Shashi, Bawa Puneet International journal of speech technology . 2021,第2期
• 5. An EM Algorithm for Training Wideband Acoustic Models from Mixed-Bandwidth Training Data [C] . Seltzer, M.L., Acero, . 2005
• 6. Large margin training of acoustic models for speech recognition. [D] . Sha, Fei. 2007
• 7. Talker-identification training using simulations of binaurally combined electric and acoustic hearing: Generalization to speech and emotion recognition [O] . Vidya Krull, Xin Luo, Karen Iler Kirk -1
• 8. TRAINING WIDEBAND ACOUSTIC MODELS USING MIXED-BANDWIDTH TRAINING DATA VIA FEATURE BANDWIDTH EXTENSION [O] . 2008

• 1. Quality Assessment of Training Data with Uncertain Labels for Classification of Subjective Domains [J] . Ying Dai . 电脑和通信（英文） . 2017,第7期
• 2. Improvement Comparison of Different Lattice-based Discriminative Training Methods in Chinese-monolingual and Chinese-English-bilingual Speech Recognition [J] . QIAN Yan-Min SHAN Yu-Xiang WANG Lin-Fang LIU Jia . 自动化学报 . 2012,第7期
• 3. Long Short-Term Memory Recurrent Neural Network-Based Acoustic Model Using Connectionist Temporal Classification on a Large-Scale Training Corpus [J] . Donghyun Lee ,Minkyu Lim ,Hosung Park . 中国通信 . 2017,第009期
• 4. A New Computer Aided Volleyball Training System Based on Pattern Recognition Model [J] . . International Journal of Technology Management . 2017,第003期
• 5. 利用自然高原进行多梯度训练的实验研究——兼论建造四川省高原训练基地的可行性 [C] . 王迪 ,谭泽芹 ,何培林 . 第四届中国多巴高原训练与健康国际研讨会 . -1
• 6. 区分性训练和区分性自适应在自动语音识别声学模型优化中的应用 [A] . 竺博 . 2009